Cloud Forensics: A Meta-Study of Challenges, Approaches, and Open Problems
University of Alabama at Birmingham
Birmingham, Alabama 35294-1170
University of Alabama at Birmingham
Birmingham, Alabama 35294-1170
Abstract—In recent years, cloud computing has become
popular as a cost-effective and efﬁcient computing paradigm.
Unfortunately, today’s cloud computing architectures are not
designed for security and forensics. To date, very little research
has been done to develop the theory and practice of cloud
forensics. Many factors complicate forensic investigations in
a cloud environment. First, the storage system is no longer
local. Therefore, even with a subpoena, law enforcement agents
cannot conﬁscate the suspect’s computer and get access to the
suspect’s ﬁles. Second, each cloud server contains ﬁles from
many users. Hence, it is not feasible to seize servers from
a data center without violating the privacy of many other
users. Third, even if the data belonging to a particular suspect
is identiﬁed, separating it from other users’ data is difﬁcult.
Moreover, other than the cloud provider’s word, there is usually
no evidence that links a given data ﬁle to a particular suspect.
For such challenges, clouds cannot be used to store healthcare,
business, or national security related data, which require audit
and regulatory compliance.
In this paper, we systematically examine the cloud forensics
problem and explore the challenges and issues in cloud foren-
sics. We then discuss existing research projects and ﬁnally, we
highlight the open problems and future directions in cloud
forensics research area. We posit that our systematic approach
towards understanding the nature and challenges of cloud
forensics will allow us to examine possible secure solution
approaches, leading to increased trust on and adoption of cloud
computing, especially in business, healthcare, and national
security. This in turn will lead to lower cost and long-term
beneﬁt to our society as a whole.
Cloud computing has emerged as a popular and inex-
pensive computing paradigm in recent years. In the last
5 years alone, we have seen an explosion of applications
of cloud computing technology, for both enterprises and
individuals seeking additional computing power and more
storage at a low cost. Small and medium scale industries
ﬁnd cloud computing highly cost effective as it replaces the
need for costly physical and administrative infrastructure,
and offers the ﬂexible pay-as-you-go structure for payment.
Khajeh-Hosseini et al. found that an organization could save
37% cost if they would migrate their IT infrastructure from
an outsourced data centre to the Amazon’s Cloud . A
recent research by Market Research Media states that the
global cloud computing market is expected to grow at an
30% Compound Annual Growth Rate (CAGR) reaching
$270 billion in 2020 . According to Gartner Inc., the
strong growth of cloud computing will bring $148.8 billion
revenue by 2014 . Cloud computing is getting popular
not only in the private industry, but also in the government
sector. According to a research from INPUT, the US Federal
government’s spending on the cloud will reach $792 million
by 2013 .
Clouds use the multi-tenant usage model and virtual-
ization to ensure better utilization of resources. However,
these fundamental characteristics of cloud computing are
actually a double-edged sword – the same properties also
make cloud-based crimes and attacks on clouds and their
users difﬁcult to prevent and investigate. According to a
recent IDCI survey, 74% of IT executives and CIOs referred
security as the main reason to prevent their migration to
the cloud services model . Some recent attacks on cloud
computing platforms strengthen the security concern. For
example, a botnet attack on Amazon’s cloud infrastructure
was reported in 2009 . Besides attacking cloud infras-
tructure, adversaries can use the cloud to launch attack on
other systems. For example, an adversary can rent hundreds
of virtual machines (VM) to launch a Distributed Denial
of Service (DDoS) attack. After a successful attack, she
can erase all the traces of the attack by turning off the
VMs. A criminal can also keep her secret ﬁles (e.g., child
pornography, terrorist documents) in cloud storage and can
destroy all evidence from her local storage to remain clean.
To investigate such crimes involving clouds, investigators
have to carry out a digital forensic investigation in the cloud
environment. This particular branch of forensic has become
known as Cloud Forensics.
According to an annual report of Federal Bureau of
Investigation (FBI), the size of the average digital forensic
case is growing at 35% per year in the United States.
From 2003 to 2007, it increased from 83GB to 277 GB in
2007 . This rapid increase in digital forensics evidence
drove the forensic experts to devise new techniques for
digital forensics. At present, there are several established
and proven digital forensics tools in the market. With the
proliferation of clouds, a large portion of these investigations
now involves data stored in or actions performed in a cloud
computing system. Unfortunately, many of the assumptions
of digital forensics are not valid in cloud computing model.
arXiv:1302.6312v1 [cs.DC] 26 Feb 2013
For example, in a cloud environment, investigators do not
have physical access to the evidence – something they
usually have in traditional privately owned and locally
hosted computing systems. As a result, cloud forensics
brings new challenges from both technical and legal point
of view and has opened new research area for security and
Contributions. In this article, we present a systematic
analysis of the cloud forensics problem. The contributions
of this paper are as follows:
• We present a systematic summary of the challenges and
issues in cloud forensics.
• We provide a comprehensive analysis of proposed so-
lutions for cloud forensics in the three different service
models of publicly deployed cloud computing.
• We also identify the usages and advantages of cloud
computing in digital forensics and enumerate the
current open problems of cloud forensics.
Organization. The rest of the article is organized as
follows: Section II provides the background knowledge of
cloud computing, digital forensics, and cloud forensics. Sec-
tion III presents the challenges in cloud forensics and section
IV discusses the existing proposed solutions. Section V
provides an evaluation of existing digital forensics tools in a
cloud environment. In Section VI, we discuss the advantages
of cloud forensics over traditional computer forensics and
Section VII describes some use cases of cloud computing
in digital forensics. Section VIII presents the open problems
of cloud forensics and ﬁnally, we conclude in Section IX.
In this section, we provide a brief overview of cloud com-
puting and computer forensics. We also discuss the unique
nature of clouds, which make digital forensics investigations
A. Cloud computing
Deﬁnition. According to the deﬁnition by the National
Institute of Standards and Technology (NIST), “Cloud com-
puting is a model which provides a convenient way of on-
demand network access to a shared pool of conﬁgurable
computing resources (e.g., networks, servers, storage, appli-
cations, and services), that can be rapidly provisioned and
released with minimal management effort or service provider
interaction” . The Open Cloud Manifesto Consortium
deﬁnes cloud computing as “the ability to control the
computing power dynamically in a cost-efﬁcient way and the
ability of the end user, organization, and IT staff to utilize the
most of that power without having to manage the underlying
complexity of the technology” .
Cloud computing has some important characteristics –
On-demand self-service, broad network access, resource
pooling, rapid elasticity, and measured service. Parkhill
proposed utility computing long ago  and Michael et
al. mention cloud computing as a new term for “computing
as a utility” . They deﬁned cloud computing as a
combination of Software-as-a-Service and utility computing,
but they consider private clouds outside of cloud computing.
Classiﬁcation according to service model. According to
the nature of service model used by the Cloud Service
Provider (CSP), cloud computing can be divided into three
categories: Software as a Service (SaaS), Platform as a Ser-
vice (PaaS), and Infrastructure as a Service (IaaS) . Figure
1 illustrates the three service models in cloud computing
Software as a Service (SaaS). This model provides the
consumers the facility of using cloud service provider’s
software application running on cloud infrastructure. This
approach is different from traditional software package dis-
tribution to individuals or organizations. In this model, there
is no need for software distribution. Consumers can access
the application through the web browsers in computers or
mobile devices. Usually, there is a monthly subscription fee
to use the service. This fee can sometimes vary according
to the number of users of an organization. In this model,
customers do not have any control over the network, servers,
operating systems, storage, or even on the application, except
some access control management for multi-user application.
Some of the examples of SaaS are : Salesforce , Google
Drive , and Google calender .
Platform as a Service (PaaS). In PaaS, customers can
deploy their own application or a SaaS application in the
cloud infrastructure. Normally, the customers pay accord-
ing to the bandwidth usage and database usage. They do
not manage or control the underlying cloud infrastructure
including network, servers, operating systems, or storage,
but have control over the deployed applications and some
application hosting environment conﬁgurations. Customers
can only use the application development environments,
which are supported by the PaaS providers. Two examples
of PaaS are: Google App Engine (GAE)  and Windows
Azure . Customers can host their own developed web
based application on these platforms.
Infrastructure as a Service (IaaS). This model allows a
customer to rent processing power and storage to launch his
own virtual machine. It alleviates the costly process of main-
taining own data center. One of the important features is that
the customers can scale up according to their requirement.
It allows their applications to handle high load smoothly.
On the other hand, they can save cost when the demand
is low. Customers have full control over operating systems,
storage, deployed applications, and possibly limited control
of selecting networking components (e.g., host ﬁrewalls).
Cloud (Web) applications!
Kernel (OS/ Apps)!
Service & APIs!
Cloud software environment!
Cloud software infrastructure!
Supporting (IT) infrastructure!
Figure 1. Three service models of Cloud Computing 
An example of IaaS is Amazon EC2 . EC2 provides
users with access to virtual machines (VM) running on its
servers. Customers can install any operating system and can
run any application in that VM. It also gives the customers
the facility of saving the VM status by creating an image
of the instance. The VM can be restored later by using that
Other service models. Motahari-Nezhad et al. proposed
a more speciﬁc service model, which is Database as a
Service (DaaS) . This is a special type of storage
service provided by the cloud service provider. Most of the
providers offer the customers to store data in a key-value
pair, rather than using traditional relational database. Also
data of multiple users can be co-located in a shared physical
table. Two of the examples of DaaS are: Amazon SimpleDB
 and Google Bigtable . The query language to store,
retrieve, and manipulate the data depends on the provider.
There is a monthly fee depending on the incoming and
outgoing volume of data and machine utilization.
Classiﬁcation according to deployment model. According
to the deployment model, cloud computing can be cate-
gorized into four categories – private cloud, public cloud,
community cloud, and hybrid cloud .
Private cloud. In private cloud model, the cloud infras-
tructure is fully operated by the owner organization. It is
the internal data center of a business organization. Usually,
the infrastructure is located at the organizations’ premise.
Private cloud can be found in large companies and for
Community cloud. If several organizations with common
concerns (e.g., mission, security requirements, policy, and
compliance considerations) share cloud infrastructure then
this model is referred as community cloud.
Public cloud. In the public cloud model, the Cloud Service
Providers (CSP) owns the cloud infrastructure and they
make it available to the general people or a large industry
group. All the examples given in the service based cloud
categorization are public cloud.
Hybrid cloud. As the name suggests, the hybrid cloud
infrastructure is a composition of two or more clouds
(private, community, or public).(e.g., cloud bursting for
load-balancing between clouds). Hybrid Cloud architecture
requires both on-premises resources and off-site (remote)
server based cloud infrastructure.
Figure 2 shows three different deployment models of
cloud computing – private, public, and hybrid cloud.
B. Computer Forensics
Computer forensics is the process of preserving, col-
lecting, conﬁrming, identifying, analyzing, recording, and
presenting crime scene information. Wolfe deﬁnes computer
forensics as “a methodical series of techniques and pro-
cedures for gathering evidence, from computing equipment
and various storage devices and digital media, that can be
presented in a court of law in a coherent and meaningful
format” . According to a deﬁnition from NIST ,
computer forensic is “an applied science to identify a
incident, collection, examination, and analysis of evidence
data”. In computer forensics, maintaining the integrity of
the information and strict chain of custody for the data
is mandatory. Several other researchers deﬁne computer
forensic as the procedure of examining computer system to
determine potential legal evidence , .
From the deﬁnitions, we can say that computer forensics
is comprised of four main processes:
• Identiﬁcation: Identiﬁcation process is comprised of
two main steps: identiﬁcation of an incident and identi-
ﬁcation of the evidence, which will be required to prove
• Collection: In the collection process, an investigator
extracts the digital evidence from different types of
media e.g., hard disk, cell phone, e-mail, and many
more. Additionally, he needs to preserve the integrity
of the evidence.
• Organization: There are two main steps in organiza-
tion process: examination and analysis of the digital
evidence. In the examination phase, an investigator
extracts and inspects the data and their characteristics.
In the analysis phase, he interprets and correlates the
available data to come to a conclusion, which can prove
or disprove civil, administrative, or criminal allegations.
• Presentation: In this process, an investigator makes an
organized report to state his ﬁndings about the case.
This report should be appropriate enough to present to
Figure 3 illustrates the ﬂow of aforementioned processes
in computer forensics.
Legal basis. Before 2006, there had been no separate US
Federal law for computer forensics investigation in civil
cases. For criminal cases, investigators still use the 1986
Computer Fraud and Abuse Prevention Act. As computer
based crime was increasing rapidly, the Advisory Committee
on Civil Rules took initiative to resolve this issue at 2000.
Finally at 2006, an amendment to Federal Rules of Civil
Procedure (FRCP) was published, which is known as e-
discovery amendment . Some important factors in the
FRCP amendment, that are contributing in today’s digital
• FRCP deﬁnes the discoverable material and introduces
the term Electronically Stored Information (ESI). Under
this deﬁnition, data stored in hard disk, RAM, or Virtual
Machine (VM) logs, all are discoverable material for
the forensic investigation.
• It introduces data archiving requirements.
• It addresses the issue of format in production of ESI. If
the responding party objects about the requested format,
then it suggests a model for resolving dispute about the
form of production.
• It provides a Safe Harbor Provision. Under the rule of
safe harbor, if someone loses data due to routine faithful
operation, then the court may not impose sanction on
her for failing to provide ESI. , .
C. Cloud forensics
We deﬁne Cloud forensics as the application of computer
forensic principles and procedures in a cloud computing
environment. Since cloud computing is based on extensive
network access, and as network forensics handles forensic
investigation in private and public network, Ruan et al.
deﬁned cloud forensics as a subset of network forensics
. They also identiﬁed three dimensions in cloud forensics
– technical, organizational, and legal. According to the
authors’ knowledge, till now this is only deﬁnition of cloud
Cloud forensics procedures will vary according to the
service and deployment model of cloud computing. For SaaS
and PaaS, we have very limited control over process or
network monitoring. Whereas, we can gain more control
in IaaS and can deploy some forensic friendly logging
mechanism. The ﬁrst three steps of computer forensics will
vary for different services and deployment models. For
example, the collection procedure of SaaS and IaaS will
not be same. For SaaS, we solely depend on the CSP to
get the application log, while in IaaS, we can acquire the
Virtual machine instance from the customer and can enter
into examination and analysis phase. On the other hand, in
the private deployment model, we have physical access to
the digital evidence, but we merely can get physical access
to the public deployment model.
III. CHALLENGES OF CLOUD FORENSICS
In this section, we examine the challenges in cloud
forensics, as discussed in the current research literature. We
present our analysis by looking into the challenges faced by
investigators in each of the stages of computer forensics (as
described in Section II-B). Some of the important challenges
we address here are: forensic data acquisition, logging,
preserving chain of custody, limitation of current forensics
tools, crime scene reconstruction, cross border law, and
On Premise Infrastructure!
Figure 2. Three different cloud deployment models
Figure 3. Computer Forensics Process Flow
A. Forensic Data Acquisition
Collection of the digital evidence is the most crucial step
of forensic procedure. Any errors that have occurred in the
collection phase will propagate to the evidence organization
and reporting phase, which will eventually affect the whole
investigation process. According to Birk, evidence can be
available in three different states in cloud – at rest, in motion,
and in execution . Data that occupies the disk space is
called data at rest. Data that can be transferred from one state
to another state is referred to as data in motion. Sometimes,
we have executable data, for example, image snapshot. We
can load and run an image snapshot to get the data in rest and
data in motion. In cloud forensics, data collection procedure
also varies depending on the service and deployment model
Some of the factors that make the data acquisition process
in cloud forensic harder than traditional computer forensics
are discussed below.
Physical Inaccessibility. Physical inaccessibility of digital
evidence makes the evidence collection procedure harder in
cloud forensics. The established digital forensic procedures
and tools assume that we have physical access to the
computers. However, in cloud forensics, the situation is
different. Sometimes, we do not even know where the data
is located as it is distributed among many hosts in multiple
data centers. A number of researchers address this issue in
their work , , , , , .
Less Control in Clouds and Dependence on the CSP. In
traditional computer forensics, investigators have full control
over the evidence (e.g., a hard drive conﬁscated by police).
In a cloud, unfortunately, the control over data varies in
different service models. Figure 4 shows the limited amount
of control that customers have in different layers for the
three service models – IaaS, PaaS, and SaaS. For this
reason, we mostly depend on the CSP to collect the digital
evidence from cloud computing environment. This is a
serious bottleneck in the collection phase.
In IaaS, users have more control than SaaS or PaaS.
The lower level of control has made the data collection in
SaaS and PaaS more challenging than in IaaS. Sometimes,
it is even impossible. If we manage to get the image of
an IaaS instance, it will make our life easy to investigate
SaaS! PaaS! IaaS!
Customers have control!
Customers do not have control!
Figure 4. Customers’ control over different layers in different service model
the system. For SaaS and PaaS, we need to depend on the
CSP. We can only get a high level of logging information
from this two service models. As customers have control
over the application deployed in PaaS, they can keep log of
different actions to facilitate the investigation procedure. On
the contrary in SaaS, customers basically have no control to
log the actions.
Dykstra et al. presented the difﬁculty of data acquisition
by using a hypothetical case study of child pornography
. To investigate this case, the forensics examiner needs
a bit-for-bit duplication of the data to prove the existence
of contraband images and video, but in a cloud, he can-
not collect data by himself. At ﬁrst, he needs to issue a
search warrant to the cloud provider. However, there are
some problems with the search warrant in respect of cloud
environment. For example, warrant must specify a location,
but in cloud the data may not be located at a precise location
or a particular storage server. Furthermore, the data can not
be seized by conﬁscating the storage server in a cloud, as the
same disk can contain data from many unrelated users. To
identify the criminal, we need to know whether the virtual
machine has a static IP. Almost in all aspects, it depends on
the transparency and cooperation of the cloud provider.
Volatile Data. Volatile data cannot sustain without power.
When we turn off a Virtual Machine (VM), all the data
will be lost if we do not have the image of the instance.
This issue is highlighted in several research works ,
, , , . Though IaaS has some advantages
over SaaS and PaaS, volatile storage can be a problem in
IaaS model if data is not always synchronized in persistent
storage, such as, Amazon S3 or EBS. If we restart or turn
off a VM instance in IaaS (e.g., in Amazon EC2), we will
lose all the data. Registry entries or temporary internet ﬁles,
that reside or be stored within the virtual environment will
be lost when the user exits the system. Though with extra
payment customers can get persistent storage, this is not
common for small or medium scale business organizations.
Moreover, a malicious user can exploit this vulnerability.
After doing some malicious activity (e.g., launch DoS attack,
send spam mail), an adversary can power off her virtual
machine instance, which will lead to a complete loss of
the volatile data and make the forensic investigation almost
impossible. Birk also mentioned a serious problem regarding
the volatile nature of evidence in cloud. The problem states
that some owner of a cloud instance can fraudulently claim
that her instance was compromised by someone else and
had launched a malicious activity. Later, it will be difﬁcult
to prove her claim as false by a forensic investigation .
Trust Issue. Dependence on the third party also poses trust
issue in investigation procedure. In the child pornography
case study, Dykstra et al. highlighted the trust issue in
collecting evidence . After issuing a search warrant,
the examiner needs a technician of the cloud provider to
collect data. However, the employee of the cloud provider
who collects data is most likely not a licensed forensics
investigator and it is not possible to guarantee his integrity
in a court of law . The date and timestamps of the data
are also questionable if it comes from multiple systems.
Dykstra et al. experimented with collecting evidence from
cloud environment. One of the shortcomings they found is
that it is not possible to verify the integrity of the forensic
disk image in Amazon’s EC2 cloud because Amazon does
not provide checksums of volumes, as they exist in EC2.
Large Bandwidth: In Section I, we have seen that the
amount of digital evidence is increasing rapidly. Guo et al.
pointed out the requirement of large bandwidth issue for time
critical investigation . The on-demand characteristic of
cloud computing will have vital role in increasing the digital
evidence in near future. In traditional forensic investigation,
we collect the evidence from the suspect’s computer hard
disk. Conversely, in cloud, we do not have physical access
to the data. One way of getting data from cloud VM is
downloading the VM instance’s image. The size of this
image will increase with the increase of data in the VM
instance. We will require adequate bandwidth and incur
expense to download this large image.
Multi-tenancy. In cloud computing, multiple VM can
share the same physical infrastructure, i.e., data for multiple
customers may be co-located. This nature of clouds is
different from the traditional single owner computer system.
In any adversarial case, when we acquire evidence two issues
can arise. First, we need to prove that data were not co-
mingled with other users’ data , . And secondly,
we need to preserve the privacy of other tenants while
performing an investigation . Both of these issues make
acquiring digital evidence more challenging. The multi-
tenancy characteristic also brings the side-channel attacks
 that are difﬁcult to investigate.
Analyzing logs from different processes plays a vital
role in digital forensic investigation. Process logs, network
logs, and application logs are really useful to identify a
malicious user. However, gathering this crucial information
in cloud environment is not as simple as it is in privately
owned computer system, sometimes even impossible. Cloud
forensic researchers have already identiﬁed a number of
challenges in cloud based log analysis and forensics ,
, . We brieﬂy discuss these challenges below.
Decentralization. In cloud infrastructure, log information
is not located at any single centralized log server; rather
logs are decentralized among several servers. Multiple users’
log information may be co-located or spread across multiple
Volatility of Logs. Some of the logs in cloud environment
are volatile, especially in case of VM. All the logs will be
unavailable if the user power off the VM instance. Therefore,
logs will be available only for certain period of time.
Multiple Tiers and Layers. There are several layers and
tiers in cloud architecture. Logs are generated in each tier.
For example, application, network, operating system, and
database – all of these layers produce valuable logs for
forensic investigation. Collecting logs from these multiple
layers is challenging for the investigators.
Accessibility of Logs. The logs generated in different
layers are need to accessible to different stakeholders of
the system, e.g., system administrator, forensic investigator,
and developer. System administrators need relevant log to
troubleshoot the system. Developers need the required log
to ﬁx the bug of the application. Forensic investigators need
logs that can help in their investigation. Hence, there should
be some access control mechanism, so that everybody will
get what they need exactly – nothing more, nothing less and
obviously, in a secure way.
Dependence on the CSP. Currently, to acquire the logs, we
extensively depend on the CSPs. The availability of the logs
varies depending on the service model. In SaaS, customers
do no get any log of their system, unless the CSP provides
the logs. In PaaS, it is only possible to get the application log
from the customers. To get the network log, database log,
or operating system log we need to depend on the CSP. For
example, Amazon does not provide load balancer log to the
customers . In a recent research work, Marty mentioned
that he was unable to get MySql log data from Amazon’s
Relational Database Service . In IaaS, customers do not
have the network or process log.
Absence of Critical Information in Logs. There is no
standard format of logs. Logs are available in heterogeneous
formats – from different layers and from different service
providers. Moreover, not all the logs provide crucial infor-
mation for forensic purpose, e.g., who, when, where, and
why some incident was executed.
C. Chain of Custody
Chain of custody is deﬁned as a veriﬁable provenance
or log of the location and possession history of evidence
from the point of collection at the crime scene to the
point of presentation in a court of law. It is one of the
most vital issues in traditional digital forensic investiga-
tion. Chain of custody should clearly depicts how the
evidence was collected, analyzed, and preserved in order
to be presented as admissible evidence in court . In
traditional forensic procedure, it starts with gaining the
physical control of the evidence, e.g., computer, and hard
disk. However, in cloud forensics, this step is not possible.
In a cloud, investigator can acquire the available data from
any workstation connected with the internet. Due to the multi
jurisdictional laws, procedures, and proprietary technology
in cloud environment, maintaining chain of custody will
be a challenge , . In a hypothetical case study of
compromised cloud based website, Dykstra et al. pointed
that as multiple people may have access to the evidence and
we need to depend on the CSP to acquire the evidence, the
chain of custody preservation throughout the investigation
process is questionable . According to Birk et al. the
chain of custody will be a problem in cloud forensic as the
trustworthiness of hypervisor is also questionable .
D. Limitations of Current of Forensic Tools
Due to the distributed and elastic characteristic of cloud
computing, the available forensic tools cannot cope up
with this environment. Some researchers highlighted the
limitations of current forensic tools in their work ,
, . Tools and procedures are yet to be developed
for investigations in virtualized environment, especially on
hypervisor level. Ruan et al. expressed the need of forensic-
aware tools for the CSP and the clients to collect forensic
E. Crime Scene Reconstruction
To investigate a malicious activity, sometimes the in-
vestigators need to reconstruct the crime scene. It helps
them to understand how an adversary launched the attack.
However, in cloud environment, that could be a problem
. If an adversary shut down her virtual instance after a
malicious activity or undeploy his malicious website, then
reconstruction of the crime scene will be impossible.
F. Cross Border Law
Multi-jurisdictional or cross border law is intensifying the
challenge of cloud forensics. Data centers of the service
providers are distributed worldwide. However, the privacy
preservation or information sharing laws are not in harmonic
throughout the world, even it may not be same in different
states of a country. Cross border legislation and cross border
red tape issues came in several cloud forensic research
works , ,  which make the evidence collection
process challenging. In particular, such a process should not
violate the laws of a particular jurisdiction. Furthermore,
the guideline of admissible evidence, or the guideline for
preserving chain of custody can vary among different re-
gions. It may happen that the attacker is accessing the cloud
computing service from one jurisdiction, whereas the data
she is accessing reside in different jurisdiction. Differences
in laws between these two locations can affect the whole
investigation procedure, from evidence collection, presenting
proofs to capture the attacker. Moreover, for multi-tenancy
case, we need to preserve the privacy of the tenants when
we collect data of other tenant, sharing the same resources.
However, the privacy and privilege rights may vary among
different countries or states.
The ﬁnal step of digital forensic investigation is presen-
tation, where an investigator accumulates his ﬁndings and
presents to the court as the evidence of a case. Challenges
also lie in this step of cloud forensics. Proving the evidence
in front of the jury for traditional computer forensics is
relatively easy compared to the complex structure of cloud
computing. Jury members possibly have basic knowledge
of personal computers or at most privately owned local
storage. But the technicalities of a cloud data center, running
thousands of VM, accessed simultaneously by hundreds of
users is far too complex for them to understand .
H. Trustworthy data retention
Large business organization and medicals cannot move
to cloud because of some compliance issues. Trustworthy
data retention is one of the mandatory compliance issues
Challenges of Cloud
Exists in Work
IaaS PaaS SaaS
Physical inaccessibility 3 3 3 , , ,
, , .
Dependence on CSP 3 3 3 
Volatile Data 3 5 5 , , ,
Trust Issue 3 3 3 , 
Large bandwidth 3 5 5 
Multi-tenancy 3 3 5 , , 
Decentralization of Logs 3 3 3 , 
Volatility of logs 3 5 5 , 
Logs in multiple tiers and
3 3 3 
Accessibility of logs 3 3 3 
Depending on CSP for
3 3 3 
Absence of critical infor-
mation in logs
3 3 3 
Chain of Custody 3 3 3 , , ,
Problem of current foren-
3 3 3 , , 
Crime scene reconstruc-
3 3 5 
Cross border law 3 3 3 , , ,
Presentation 3 3 3 
Compliance issue 3 3 5 
SUMMARY OF CHALLENGES IN CLOUD FORENSICS
that has direct impact on digital forensics. Hasan et al. state
that trustworthy data retention should provide the long-term
retention and disposal of organizational record to prevent
unwanted deletion, editing, or modiﬁcation of data during
the retention period. It should also prevent recreation of
record once it has been removed . While there are still
some open problems to ensure the secure data retention
at storage level, the cloud computing model imposes some
new challenges. Popovic et al. mentioned some issues about
retention and destruction of record in cloud computing. For
example, who enforces the retention policy in the cloud,
and how are exceptions, such as, litigation holds managed?
Moreover, how can the CSPs assure us that they do not
retain data after destruction of it ? There are several
laws in different countries, which mandate the trustworthy
data retention. Just in United States, there are 10,000 laws
at the federal and state levels that force the organizations
to manage records securely . Some of the laws and
regulations are stated below:
• Sarbanes-Oxley Act: This act mandates public compa-
nies to provide disclosure and accountability of their
ﬁnancial reporting, subject to independent audits .
• The Health Insurance Portability and Accountability
Act (HIPAA): This act requires privacy and conﬁden-
tiality of patient medical record .
• The Securities and Exchange Commission (SEC) rule
17a-4: According to this law, traders, brokers, and
ﬁnancial companies need to maintain their business
records, transactions, and communications for a number
of years .
• Federal Information Security Management Act: This
law regulates information systems used by the Federal
government and afﬁliated parties .
• The Gramm-Leach-Bliley Act:According to this law,
ﬁnancial institutions must have a policy to protect
information from any predictable threats in integrity
and data security .
• European Commission data protection legislation:
In 2012, European Commission proposes major
reformation of the 1995 data protection legislation
to strengthen the privacy and conﬁdentiality of
individuals’ data .
All of the above laws mandate trustworthy data reten-
tion. Compliance with all of these laws is challenging in
cloud computing environment. For example, the SOX act
mandates that the ﬁnancial information must be resided
in an auditable storage, which the CSPs do not provide.
Business organization cannot move their ﬁnancial informa-
tion to cloud infrastructure as it does not comply with
the act. As cloud infrastructure does not comply with
HIPAA’s forensic investigation requirement, hospitals cannot
move the patients’ medical information to cloud storage.
As business and healthcare organizations are the two most
data consuming sectors, cloud computing cannot achieve
the ultimate success without including these two sectors.
These sectors are spending extensively to make their own
regulatory-compliant infrastructure. A regulatory-compliant
cloud can save this huge investment. Hence, we need to
solve the audit compliance issue to bring more customers in
Table I gives an overview of the challenges in three
service models of cloud computing for publicly deployed
IV. CURRENT SOLUTIONS
In this section, we discuss some existing proposed solu-
tions, which can mitigate some of the challenges of cloud
A. Trust Model
In Section III, we have already seen that for forensic
data acquisition, we need to depend on the CSP heavily.
This inevitably affects trust and evidence integrity. Dykstra
et al. proposed a trust model with six layers: Guest appli-
cation / data, Guest OS, Virtualization, Host OS, Physical
hardware, and Network. The further down the stack is, the
less cumulative trust is required. For example, in Guest OS
layer, we require trust in Guest OS, hypervisor, host OS,
hardware, and network layer. While, in network layer, we
require trust in only the network. Examiners can examine
evidence from different layer to ensure the consistency of
the digital evidence. For forensic examination, we need to
choose the appropriate layer, which depends on the data
available in the layer and trust in the available data .
Wolthusen suggested an interactive evidence presentation
and visualization mechanism to overcome the trust issue
. Ko et al. proposed TrustCloud – a trust preserving
framework for cloud .
B. Integrity Preservation
Integrity preservation of the digital evidence is a crucial
step in cloud forensic investigation. Without integrity preser-
vation, the validity of the evidence will be questionable and
the jury can object about it. Generating a digital signature on
the collected evidence and then checking the signature later
is one way to validate the integrity. As data is distributed
among multiple servers, this procedure is not simple, rather
quite complicated. However, cloud researchers proposed
some mechanisms to generate and check the signature of
distributed cloud data.
Hegarty et al. also proposed a distributed signature de-
tection framework that will facilitate the forensic investi-
gation in cloud environment . Traditional techniques
of signature detection for digital forensic are not efﬁcient
and appropriate due to the distributed nature of cloud com-
puting. Current model of ﬁle storage is comprised of two
components – Meta data Servers (MDS) and Object Storage
Devices (OSD). The hash value of each ﬁle is stored in the
MDS as an e-tag and integrity is checked each time after
uploading / downloading a ﬁle. In the proposed framework,
the ﬁrst step is to send a list of target buckets to the Forensic
Cluster Controller (FCC), along with a ﬁle containing the
target MD5 hash values. The FCC then initializes and
queries to Analysis Nodes (AN) for getting the number of
ﬁles contained in targeted bucket. Upon receiving the round
one signature ﬁle from FCC, each AN retrieves the e-tags
of the bucket. The signatures in the round one signature
ﬁle are compared with the signatures generated from the e-
tags by the AN. After getting feedback from all ANs, FCC
terminates the ANs. They tested their framework by two
ways – using Amazon S3 and by emulating a cloud platform.
They achieved zero false positive and false negative rate and
found signiﬁcant improvement in terms of data required at
Log information is vital for forensic investigations. A lot
of researchers have explored logging in the context of a
cloud. Marty proposed a log management solution, which
can solve several challenges of logging as discussed in
Section III . In the ﬁrst step of the logging solution,
logging must be enabled on all infrastructure components to
collect logs. The next step is for establishing a synchronized,
reliable, bandwidth efﬁcient, and encrypted transport layer to
transfer log from the source to a central log collector. The
ﬁnal step deals with ensuring the presence of the desired
information in the logs. The proposed guideline tells us to
focus on three things: when to log, what to log, and how
to log. The answer to when to log depends on the use
cases, such that business relevant logging, operations based
logging, security (forensics) related logging, and regulatory
and standards mandates. At minimum, he suggested to log
the timestamps record, application, user, session ID, severity,
reason, and categorization, so that we can get the answer of
what, when, who, and why (4 W). He also recommended
syntax for logging, which was represented as a key-value
pair and used three ﬁelds to establish a categorization
schema – object, action, and status. He also implemented an
application logging infrastructure at a SaaS company, where
he built a logging library that can be used in Django. This
library can export logging calls for each severity level, such
layer, he built an AJAX library to store the logs in server
side. Then he tuned the apache conﬁguration to get the logs
in desired format and to get the logs from load balancer.
For logging the back-end operations, he used Log4j as the
backend was built in java. While there are several advantages
to this approach, this work does not provide any solution
about logging network usage, ﬁle metadata, process usage,
and many other important evidence, which are important for
forensic investigation in IaaS and PaaS models.
To facilitate logging in clouds, Zafarullah et al. proposed
logging provided by OS and the security logs . In order
to investigate digital forensics in cloud, they set up cloud
computing environment by using Eucalyptus. Using Snort,
Syslog, and Log Analyzer (e.g., Sawmill), they were able to
monitor the Eucalyptus behavior and log all internal and
external interaction of Eucalyptus components. For their
experiment, they launched a DDoS attack from two virtual
machine and analyzed bandwidth usage log and processor
usage log to detect the DDoS attack. From the logs in
/var/eucalyptus/jetty-request-05-09-xx ﬁle on Cloud Con-
troller (CC) machine, it is possible to identify the attacking
machine IP, browser type, and content requested. From
these logs, it is also possible to determine the total number
of VMs, controlled by single Eucalyptus user and VMs
communication patterns. Their experiment shows that if the
CSPs come forward to provide better logging mechanism,
cloud forensics will be beneﬁted greatly.
To get necessary logs from all the three service models,
Bark et al. proposed that CSP could provide network, pro-
cess and access logs to customer by read only API . By
using these APIs, customer can provide valuable information
to investigator. In PaaS, customers have full control on their
application and can log variety of access information in a
conﬁgurable way. Hence for PaaS, they proposed a central
log server, where customer can store the log information. In
order to protect log data from possible eavesdropping and
altering action, customers can encrypt and sign the log data
before sending it to the central server.
D. Cloud Management Plane
Data acquisition from cloud infrastructure is a challenging
step in cloud forensics. CSPs can play a vital role in this step
by providing a web based management console like AWS
management console. Dykstra et al. recommended a cloud
management plane for use in IaaS model . From the
console panel, customers as well as investigators can collect
VM image, network, process, database logs, and other digital
evidence, which cannot be collected in other ways. Only
problem with this solution is that, it requires an extra level of
trust – trust in the management plane. In traditional evidence
collection procedure, where we have physical access to the
system, this level of trust is not required.
E. Solution of Legal Issues
Legal issue is a great obstacle in cloud forensics. Cross
border legislations often hinder the forensic procedures.
At present, there is a massive gap in the existing Service
Level Agreement (SLA), which neither deﬁnes the respon-
sibility of CSPs at the time of some malicious incident,
nor their role in forensic investigation. Researches gave
emphasis on sound and robust SLA between cloud service
providers and customers , , . To resolve the
transparency issues, the CSP should build a long-term trust
relationship with customers. A robust SLA should state
how the providers deal with the cyber crimes, i.e., how
and to which extent they help in forensic investigation
procedure. In this context, another question can come – how
we can be sure of the robustness of a SLA. To ensure the
quality of SLA, we can take help from a trusted third party
. To overcome the cross border legislation challenges,
Biggs proposed an international unity for introducing an
international legislation for cloud forensics investigation
. By implementing a global law throughout the world,
we can make the investigation procedure smooth enough to
complete in a time limit.
F. Virtual Machine Introspection
Virtual Machine Introspection (VMI) is the process of
externally monitoring the runtime state of VM from either
the Virtual Machine Monitor (VMM), or from some virtual
machine other than the one being examined. By runtime
state, we are referring to processor registers, memory, disk,
network, and other hardware-level events. Through this
process, we can execute a live forensic analysis of the
system, while keeping the target system unchanged . In
Proposed Solution Suitable for Work
IaaS PaaS SaaS
Trust Model 3 3 5 , ,
Distributed signature detec-
5 5 3 
Log management solution 5 3 5 
OS and the security logs 3 5 5 
API provide by CSP for
3 3 3 
Cloud management plane 3 3 3 
Robust SLA 3 3 3 , ,
Trusted Third Party 3 3 3 
Global unity 3 3 3 
Virtual Machine Introspec-
3 5 5 
Continuous synchronization 3 3 5 
Trusted Platform Module
3 3 5 , 
Isolating a Cloud Instance 3 5 5 
Data Provenance in Cloud 3 3 3 , 
SUMMARY OF SOLUTIONS IN CLOUD FORENSICS
this work, Hay et al. showed that if a VM instance is com-
promised by installing some rootkit to hide the malicious
events, it is still possible to identify those malicious events
by performing VMI. They used an open source VMI library,
Xen (VIX) suite to perform their experiment. However, this
tool is no longer maintained under this name, it is now
known as LibVMI .
G. Continuous Synchronization
In order to provide the on demand computational and
storage service, CSPs do not provide persistent storage to
VM instance. If we turn off the power or reboot the VM,
we will eventually lose all the data reside in the VM. To
overcome the problem of volatile data, Birk et al. mentioned
about the possibility of continuous synchronization of the
volatile data with a persistent storage . However, they
did not provide any guideline about the procedure. There
can be two possible ways of continuous synchronization.
• CSPs can provide a continuous synchronization API
to customers. Using this API, customers can preserve
the synchronized data to any cloud storage e.g., Ama-
zon S3, or to their local storage. Implementing this
mechanism will be helpful to get the evidence from
a compromised VM, even though the adversary shut
down the VM after launching any malicious activity.
• However, if the adversary is the owner of a VM, the
above-mentioned mechanism will not work. Trivially,
she will not be interested to synchronize her malicious
VM. To overcome this issue, CSPs by themselves can
integrate the synchronization mechanism with every
VM and preserve the data within their infrastructure.
Challenges Proposed Solution
Trust issue for depending on CSP Trust Model
Preserving integrity Distributed signature detection
Decentralization of logs, logs in mul-
tiple tiers and layers, absence of crit-
ical information in logs, Volatility of
Log management solution
Depending on CSP for logs API provide by CSP for logs
Dependability on CSP for data acqui-
Cloud management plane
Compliance issue, dependability on
Compliance issue, Developing a ro-
Trusted third party
Cross border law Global unity
Live forensics issue Virtual machine introspection
Volatile Data Continuous synchronization
Trust issues of cloud computing Trusted platform module
Multi-tenancy issue Isolating a cloud instance
Chain of custody Data provenance in cloud
ANALYSIS OF CHALLENGES AND PROPOSED SOLUTIONS
H. Trusted Platform Module (TPM)
To preserve the integrity and conﬁdentiality of the data,
several researchers proposed TPM as the solution ,
. TPM for cloud computing was proposed by several
researchers previously for ensuring trust in cloud computing
, . By using TPM, we can get machine authenti-
cation, hardware encryption, signing, secure key storage,
and attestation. It can provide the integrity of the running
virtual instance, trusted log ﬁles, and trusted deletion of
data to customers. However, Dykstra et al. mentioned that
TPM is not totally secure and it is possible to modify a
running process without being detected by TPM. Moreover,
at present, CSPs have heterogeneous hardware and few of
them have TPM. Hence, CSPs cannot ensure a homogeneous
hardware environment with TPM in near future.
I. Isolating a Cloud Instance
A cloud instance must be isolated if any incident take
place on that instance. Isolation is necessary because it
helps to protect evidence from contamination. However, as
multiple instances can be located in one node, this task
becomes challenging. Delport et al. presented some possible
techniques of cloud isolation . Moving a suspicious in-
stance from one node to another node may result in possible
loss of evidence. To protect evidence, we can move other
instances reside in the same node. The ﬁrst technique that is
proposed is instance relocation. To move an instance, data on
the secondary storage, content of the virtual memory, (e.g.,
swap memory), and the running processes must be moved.
Relocation can be done in two ways – manual and automatic.
In the manual mode, the administrator has all the power to
move the instance. In automatic mode, the CSP move the
instance from one node to another. While moving, the chal-
lenge is to ensure conﬁdentiality, integrity, and availability
of other users’ data. The second technique is server farming,
which can be used to re-routing request between user and
node. The third technique is failover, where there is at least
one server that is replicating another. There are three ways
of failover – Client-based failover, DNS-based failover and
IP-address takeover. Address relocation is another technique,
which is actually a special case of DNS-based failover. When
it is detected that the main computer has failed, the trafﬁc
is rerouted to the backup server. However, this procedure
depends on the success of replication. We can also isolate an
instance by placing it in a sandbox. One approach of creating
a sandbox is installing a sandboxing application in cloud
operating system. Another approach is creating a virtual
box around an instance and observe all the communication
channel. The third technique is placing a Man in the Middle
(MITM) between cloud instance and hardware. In that way,
we can get log information from CPU, RAM, hard drive,
and network. To get beneﬁt from this mechanism, the CSP
should embrace this technique for implementation in its
J. Data Provenance in Cloud
Provenance provides the history of an object. By im-
plementing secure provenance, we can get some impor-
tant forensic information, such as, who owns the data at
a given time, who accesses the data, and when. Some
researchers have applied the principles of provenance to
cloud forensics , . Secure provenance can ensure
the chain of custody in cloud forensics as it can provide
the chronological access history of evidence, how it was
analyzed, and preserved. There have been several projects
for secure provenance in cloud computing , , but
no CSP has practically implemented any of the mechanisms
Table II gives an overview of the solution in three different
service model of cloud computing. Table III provides an
analysis of challenges and solution, i.e. which solution is
applicable to overcome which challenge.
V. EVALUATION OF CURRENT FORENSIC TOOLS IN
There are some popular and proven digital forensics tools
used by forensic investigators, e.g., Encase, Accessdata FTK,
and others. Though the data acquisition procedure is differ-
ent in a cloud environment compared to traditional computer
forensics, these tools can be used for data acquisition from
cloud environment. So far, there has been a single work
that evaluates the capability of some available forensic tools
in cloud environment . To evaluate the capability of
forensic tool, Dykstra et al. mostly focus on data acquisition
step. They chose Amazon EC2 for their experiment, and
used EnCase and Accessdata FTK to remotely acquire foren-
sic evidence. They conducted three experiments to collect
data from three different layers and got success in all the
experiments. In the ﬁrst experiment, they collected forensic
data remotely from the guest OS layer of cloud. Encase
Servlets and FTK Agents are the remote programs, which
were used to communicate and collect data. For the second
experiment, they prepared an Eucalyptus cloud platform and
collected data from the virtualization layer. In the third
experiment, they tested the acquisition at the host operating
system layer by Amazon’s export feature. They found that
though it is possible to export data from S3, it is not possible
Though cloud forensics is a complicated process and
imposes new challenges in digital forensic procedure, it
offers some advantage over traditional computer forensics.
Several researchers highlight the availability of computing
environment through VM, which can be helpful to acquire
the computing environment for forensic investigation ,
. We can use the VM image to use as a source of digital
evidence. The computation and storage power of cloud com-
puting can also boost up the investigation process , .
Cloud computing can reduce the time for data acquisition,
data copying, transferimg and data cryptanalysis. Forensic
image veriﬁcation time will be reduced if a cloud application
generates cryptographic checksum or hash. Ruan et al.
highlighted some advantages of cloud forensics, such as, cost
effectiveness, data abundance, overall robustness, scalability
and ﬂexibility, standards and policies, and forensics-as-a-
service . If the CSPs integrate forensic facilities in cloud
environment, or they offer forensics-as-a-service to the cus-
tomer by utilizing the immense computing power, then the
customers do not need to implement any forensic schemes.
In that way, cloud forensics will be cost effective for small
and medium scale enterprise. Currently, Amazon replicates
data in multiple zones to overcome the single point failure.
In case of data deletion, this data abundance can be helpful to
collect evidence. Amazon S3 automatically generates MD5
hash of an object when we store the object in S3, which
removes the need of external tools and reduces the time
for generating hash. Amazon S3 also provides versioning
support. From the version log, we can get some crucial
information for investigation, such as, who accessed the data,
and when, what was the requestor’s IP, and what was the
change in a speciﬁc version. Roussev et al. showed that for
large-scale forensics analysis, cloud computing outperforms
the tradition forensic computing technique .
VII. CLOUD COMPUTING USAGE IN DIGITAL FORENSICS
While cloud computing model often makes digital foren-
sics difﬁcult, the use of cloud computing technology can
also facilitate the traditional digital forensic investigation
procedure. Lin et al. proposed an RSA signature based
scheme, where they showed how we can use the RSA
signature scheme to safely transfer data from mobile devices
to cloud storage . It ensures the authenticity of data and
thus helps in maintaining a trustworthy chain of custody in
forensic investigations. By using RSA signature protocol, a
veriﬁer can verify the evidence in the court. They described
the steps of uploading the digital evidences to the forensic
data center preserving privacy and downloading for veriﬁca-
tion. In this process, the cloud computing center computes
the RSA signature and send the signature to cloud storage
center, which save the ﬁnal output. The ﬁnal output can
later be downloaded to check the integrity of the data. They
conducted an experiment of their method in both cloud and
traditional environment and get better result in cloud.
Buchanan et al. proposed a cloud-based Digital Foren-
sics Evaluation Test (D-FET) platform to measure the per-
formance of the digital forensics tools . The quality
metrics are: true-positives, false-positives, and operational
performance (e.g., the speed of success, CPU utilization,
and memory usage). They described how they set up the
virtualization environment and how they ran their experi-
VIII. OPEN PROBLEMS
In Section IV, we have seen that researchers have pro-
posed several solutions to mitigate some challenges. Unfor-
tunately, only a few of the proposed solutions have been
tested with real world scenarios. Besides that, to the best
of the authors’ knowledge, CSPs have not adopted any of
the proposed solution yet. There are a good number of
open problems. Cloud management plane or API to get
the necessary logs can decrease the dependence on CSP.
However, as we do not have physical access, we still need
to depend on CSP for various forensic data acquisition
purposes, e.g., collecting temporary registry logs, identifying
deleted ﬁles from hard disk, etc. Therefore, diminishing the
dependence on CSP is still unsolved.
Limited bandwidth is another critical issue. If the cloud
storage is too high then bandwidth will be a challenge for
time critical case. This issue has not been resolved yet.
Several researchers have proposed secure data provenance
to mitigate the chain of custody issue. However, no concrete
work has been done yet, which can show how we can pre-
serve the chain of custody by secure provenance. To mitigate
the cross border issue, researchers have proposed global
unity, but there is no guideline about how this will turn
out into reality. Moreover, no solution has been proposed for
crime scene reconstruction or presentation issues. Modifying
the existing forensic tools, or creating new tools to cope up
with cloud environment is another big issue that has not
been resolved yet.
Several researchers also discussed some open problems
of cloud forensics. About logging issue in cloud forensics,
Overcome the dependence on the CSP
Making proof of concept for cloud management plane and forensics-
Acquiring large volume of data remotely for time critical case
Preserving chain of custody by secure data provenance
Guideline and implementation of global unity to overcome the cross
Crime scene reconstruction in cloud environment
Modifying the existing forensics tools to cope up with cloud paradigm
Identifying the precise location and jurisdiction of certain datum
Security visualization of logs
Forensic time line analysis of logs
Log review, log correlation and policy monitoring
OPEN PROBLEMS OF CLOUD FORENSICS AT A GLANCE
Marty proposed some open research topics in application
level logging, which are: security visualization, forensic
time-line analysis, log review, log correlation, and policy
monitoring . Wolthusen identiﬁed another critical open
problem, which is identifying the precise location and juris-
diction under which a certain datum lies .
Table IV provides an overview of the open problems in
With the increasing use of cloud computing, there is
an increasing emphasis on providing trustworthy cloud
forensics schemes. Researchers have explored the challenges
and proposed some solutions to mitigate the challenges. In
this article, we have summarized the existing challenges
and solutions of cloud forensics to answer the question –
Where does cloud forensics stand now? Current research
efforts suggest that cloud forensics is still in its infancy.
There are numerous open problems that we have mentioned
in Section VIII. By analyzing the challenges and existing
solutions, we argue that CSPs need to come forward to
resolve most of the issues. There is very little to do from
the customers’ point of view other than application logging.
All other solutions are dependent on CSPs and the policy
makers. For forensics data acquisition, CSPs can shift their
responsibility by providing robust API or management plane
to acquire evidence. Legal issues also hinder the smooth
execution of forensic investigation. We need a collaborative
attempt from public and private organizations as well as
research and academia to overcome this issue. Solving all the
challenges of cloud forensics will clear the way for making
a forensics-enabled cloud and allow more customers to take
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